Maternal and infant health insights &amp; cognitive intelligence (mihic) system and score to predict the risk of maternal, fetal, and infant morbidity and mortality

ABSTRACT

The MIHIC system in various embodiments described herein helps clinicians predict the risk of maternal mortality by detecting diseases early and identifying possible risks in mothers, fetuses and infants across pre, peri and post-natal stages of pregnancy. The system quantifies risk as a single MIHIC score, which through quantification assigns possible risks to the mother, fetus and infant. The MIHIC score uses a specialized algorithm to derive the individual and overall risk as a value between 0 and 1 and uses the risk scores to stratify the patients into High, Medium and Low risk for preventive intervention and improved pregnancy outcome.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a Continuation of U.S. patent application Ser. No.16/703,790, filed on Dec. 4, 2019, which claims priority to IndianPatent Application No. 201941042550, filed on Oct. 20, 2019, both ofwhich are incorporated by reference herein.

TECHNICAL FIELD OF THE INVENTION

The field in general relates to risk assessment of maternal, fetal andinfant health and preventive treatment in pregnant women, fetuses andinfants. Particularly it relates to the determination of pregnancyrelated risk factors in a woman while pregnant and upon childbirth andaccordingly devices preventive treatment strategies and/or personalizedmedicine to the at-risk mother/fetus/infant.

BACKGROUND

Preventive and personalized medicine are critical in improvinghealthcare delivery. Ability to prevent a disease is predicated uponidentifying all the possible risks that can lead to the manifestation ofthat particular disease. These insights about possible risks can then beused by caregivers, payers and policy makers to identify the individualsand populations with higher maternal risk and use these insights todetermine the intervention to either prevent the disease or managementof treatment for disease (if it can't be prevented completely). As anexample, if all the pregnant mothers with high risk of pre-termeclampsia can be identified, then the Clinicians and payers can work inconjunction to define a pro-active approach comprising of clinical(specific drugs, recommending more suitable care facilities, morefrequent monitoring of bio-markers etc.) and/or non-clinicalinterventions (specific changes to life-style etc.) to ensure the riskis eliminated or mitigated significantly.

The ability to determine the risks in an individual patient will dependon the ability to bring as many data points as possible about thepatient including, but not limited to: patient's health history,genetics, life-style data, socio-economic data, Clinician-patientinteractions (notes, audios, videos), epidemiology data, environmentaland social media data. It is imperative that such comprehensive analysesof data can be carried out using advanced computing capabilitiesincluding Artificial Intelligence (Natural Language processing, neuralnetworks, deep learning etc.), Quantum Mechanics, Re-enforcementlearning and advanced mathematics (graph theory, Ricci flows etc.) toprocess/analyze these vast amounts of diverse data and detect possiblepatterns of correlation and causality. These insights, when quantifiedas easily readable risk scores, can help caregivers and policy makers todetect the possible patterns that can lead to high risk of diseasemanifestation and progress in patients but also help them to recommendtreatments.

The continued success of these solutions will also depend on howsuccessfully the system can learn and adopt the real-time feed-back andincorporate it for future predictions.

According to the World Health Organization's report, Maternal mortalityaround the world is an unacceptably high 830 deaths per day, resultingfrom pregnancy or childbirth-related complications (WHO Report “GlobalHealth Observatory (GHO) data—Maternal and reproductive health” 2015).Though a number of countries in sub-Saharan Africa have halved maternalmortality levels since 1990 and countries in other regions, includingAsia and North Africa have made even greater headway, the globalmaternal mortality ratio (the number of maternal deaths per 100,000 livebirths) declined by only 2.3% per year between 1990 and 2015. Accordingto the estimate approximately 303,000 women either died during pregnancyor after childbirth in 2015.

The high number of maternal deaths in some parts of the world is due tothe lack of proper access to health services and highlights the gapbetween rich and poor. Almost all maternal deaths (99%) occur indeveloping countries with more than half of these deaths in sub-SaharanAfrica and almost one third in South Asia. The maternal mortality ratioin developing countries in 2015 is reported to be 239 per 100,000 livebirths versus 12 per 100,000 live births in developed countries. Thereare large disparities not only between countries, but also withincountries, and between women with high and low income and those womenliving in rural versus urban areas.

On an average, women in developing countries have more pregnancies thanwomen in developed countries and their lifetime risk of death due topregnancy is higher. The risk of maternal mortality is highest inadolescent girls under the age of 15 and complications in pregnancy andchildbirth is a leading cause of death amongst adolescent girls indeveloping countries. The lifetime risk of maternal death in a15-year-old woman in developed countries is 1 in 4900, whereas the samein developing countries is 1 in 180. (“Global, regional, and nationallevels and trends in maternal mortality between 1990 and 2015, withscenario-based projections to 2030: a systematic analysis by the UNMaternal Mortality Estimation Inter-Agency Group. Alkerna L, Chou D,Hogan D, Zhang S, Moller A B, Gemmill A, et al. Lancet. 2016; 387(10017): 462-74. doi.org/10.1016/S0140-6736(15)00838-7)

Most of the pregnancy and childbirth related complications that lead todeath, are either preventable or treatable. Certain pre-pregnancycomplications might worsen during pregnancy, especially if not managedas part of the woman's care and some are caused by or associated withdiseases such as malaria and AIDS, acquired during pregnancy. While mostmaternal deaths are preventable, they are still high and increasinggiven the fact that the health-care solutions to prevent or managecomplications are well known.

Therefore, there exists a long felt need for a maternal mortalitypreventing system that addresses all levels of maternal and infanthealth and provides personalized treatment strategies.

BRIEF DESCRIPTION OF DRAWINGS

An understanding of the features and innovations of the presentdisclosure will be realized by reference to the accompanying drawings.The drawings are intended to illustrate, not limit, the presentteachings. Various embodiments of the claimed invention incorporatingteachings of the present disclosure can be shown and described withrespect to the drawings herein, in which:

FIG. 1 —illustrates the overview of the MIHIC System.

FIG. 2 —illustrates the different Types of Input Data.

FIG. 3 —gives the diagrammatic representation of AI Algorithms that helppredict the MIHIC Risk Score, in one embodiment.

FIG. 4 —gives the diagrammatic representation of MIHIC AI Suite'sMachine Learning Models, in one embodiment.

FIG. 5 —illustrates the MIHIC Platform.

FIG. 6 —is a graphical representation of the maternal and infantmortality rate which depicts there were 12 maternal deaths per 100,000births and 22 infant deaths per 1000 births.

FIG. 7 —is a graphical representation of distribution of pregnant womenby causes of death which were Eclampsia (16.67%), High Fever (25%) andHemorrhage (58.33%).

FIG. 8 —is a graphical representation of type of infant deliveries whichwere preterm (28.15%) and full term (71.85%) expressed as a percentagefor a sample size of 1000 simulated records.

FIG. 9 —is a graphical representation of number of pregnant women acrossdifferent geolocations by MIHIC risk level.

FIG. 10 —is a graphical representation of % age of pregnant women acrossdifferent pregnancy risks by MIHIC Risk Levels.

FIG. 11 —is a graphical representation of % age of pregnant women acrossraces with different risk levels for Preeclampsia.

FIG. 12 —is a graphical representation of association of Preeclampsiawith risk factor urine protein.

FIG. 13 —is a graphical representation of association of Preeclampsiawith biomarker Placenta Growth Factors (PIGF).

FIG. 14 —is a graphical representation of association between Mothers'Blood Pressure and foetal heart rate with preeclampsia risk levels.

FIG. 15 —is a graphical representation of Urine Protein andProtein-Creatinine ratio vs. Preeclampsia risk levels.

FIG. 16 —is a graphical representation of correlation between variouspregnancy risks and MIHIC score.

SUMMARY OF THE INVENTION

Accordingly, in various embodiments of the claimed invention, theinvention herein relates to a Maternal and Infant Health Intelligenceand Cognitive Insights (MIHIC) scoring system for predicting andstratifying pregnant mothers into High, Medium and Low risk categoriesthrough its MIHIC scores.

The MIHIC system predicts the risks to mother, fetus and infant earlyenough during the pregnancy, before the risks actually manifest, so asto drive interventions in the patients identified that have a high riskprobability. These insights empower the clinicians, caregivers, payersand policymakers to intervene early and pro-actively to manage therisks.

The MIHIC system, in various embodiments as disclosed herein, uses thedata related to the available risk factors that cause maternal mortalityin women during pregnancy, child birth and post pregnancy. It providesspecific risk factors in each individual pregnant woman thereby helpingthe Clinicians to devise patient specific preventive treatmentstrategies. The MIHIC system as disclosed herein not only helps theClinicians to devise patient specific preventive treatment strategies toprevent pregnancy related deaths but also to devise treatment strategiesthat aid in the delivery of a healthy child. Accordingly, the MIHICsystem improves healthcare outcomes in mother, fetus and child bydecreasing maternal and infant mortality rates, while improving otherindicators of maternal and infant health by enabling early interventionsin risky pregnancies.

The MIHIC system helps the Clinicians in providing preventive strategiesbased on the risks identified and therefore reduces the cost ofinterventions by targeted interventions in high risk pregnancies asagainst performing unnecessary interventions in all pregnant women,specifically late interventions. Furthermore, the MIHIC system helpsClinicians in managing Low risk pregnancies in terms of costs and timeof the caregivers.

The MIHIC system helps Clinicians detect the risk of maternal mortalityin women by understanding and quantifying risk as a single score. MIHICscores are statistically computed at a patient (a pregnant mother) levelby calculating specific scores of the mother, fetus and infant for adefined set of risks and then statistically deriving the overall MIHICscore. The scoring engine takes into account both structured data(including, but not limited to, i.e., bio-chemistry or bio-markers,socio-economic and demographic) and unstructured data (including, butnot limited to, i.e. images, genetics, Clinician notes/images/videos,social media) and applies advanced Artificial Intelligence and DeepLearning methods (including, but not limited to, Neural Networks,Bayesian Networks, Decision Trees, Random Forests, Multi-variateCorrelational Analysis etc.) to generate the scores. Clinicians providetheir feed-back on actual risks observed and the models learn from thoseinputs and applied the learning to new MIHIC scoring that can begenerated for future patients.

The MIHIC system thus helps to reduce maternal mortality by providing aMIHIC score derived from evidence-based clinical and programmaticevaluation of pregnancy risk factors and thereby helps to provide a moreaffordable and customized preventive treatment.

The MIHIC system for early detection and assessment of maternal, fetaland infant health risk factors as disclosed herein, comprises of (i)data acquisition modules for acquisition of data from multiple sourcesin multiple range of digital formats; (ii) data pre-processing modules;(iii) a suite of Artificial Intelligence algorithms to assess thematernal, fetal and infant health risk factors; (iv) a set ofinteractive dashboards for providing graphical illustrations ofindicators and measures of maternal, fetal and infant health; and (v) aninteractive web interface, featuring diverse characteristics of pregnantwomen and to display the statistical measures of risk factors. The MIHICsystem provides the statistical measures of the maternal, fetal andinfant health risk factors in the form of a MIHIC Score for summarizingthe maternal, fetal and infant health status/condition of pregnantwomen, their fetus and infant.

The data acquisition modules collect data of pregnant women comprisingdemographics, medical, clinical and genetic data from informationsystems of clinics, laboratories, pharmacy, health insurances and othergovernment public data sources, by leveraging scraping techniques forextracting data from the multiple sources, and transform them intostructured data format before storing in local storage devices.

The data pre-processing modules perform data augmentation for (i)structured data—to reduce data inaccuracy, noise and inconsistency, (ii)unstructured data—to format data conversion, elimination of stopwords/punctuations/non-ascii characters, identification of stem wordsand lemmatization, and (iii) images.—for better data imagerepresentation.

The suite of AI algorithms comprise a set of machine learningmodels/techniques trained to learn to extract information/data from thestructured and unstructured data, assemble knowledge from the extracteddata and map the assembled data to the characteristics of associatedmaternal, fetal and infant risks to perform a risk prediction, whereinthe machine learning models are selected from, but not limited to,logistic regression, Support Vector Machine (SVM) regression and neuralnetworks which include but not limited to, convolutional neural network(CNN), recurrent neural network (RNN) and long short-term memory model(LSTM).

The interactive dashboard comprises of (i) Graphical depiction ofindicators about pregnant women's maternal, fetal and infant healthconditions, (ii) Graphical illustration of prevalence of risk factorsassociated with a risk of interest across different population groups,(iii) Charts showing associations and correlations between risk factorsaffecting Maternal health of pregnant women across different populationgroups, and/or (iv) Charts showing associations and correlations betweenrisk factors affecting Infant health across different population groups.

The interactive web interface comprises of (i) an input platform forinput of data by Clinicians and pregnant women and (ii) a displayplatform for providing the insights regarding the maternal healthcondition rendered as risk scores in a webpage and (iii) a displayplatform for providing the insights regarding the maternal healthcondition rendered as graphical charts on the interactive dashboardusing various risk indicators of pregnant women.

Also disclosed herein, is a method of detecting and assessing insightsfor maternal, fetal and infant health risk factors using a maternal andinfant health intelligence and cognitive insights (MIHIC) scoring systemcomprising (i) acquiring/capturing demographic, medical, image, clinicaland genetic data/characteristics of pregnant woman from multiplesources; (ii) identifying data formats of the acquired/captured data andsegregating the data into structured and unstructured data; (iii)pre-processing and cleaning the structured, unstructured and image dataand assembling the pre-processed data; (iv) forwarding thepre-processed/cleaned data to the AI suite for exploration of factorsassociated with risks; (v) computing MIHIC scores for various types ofrisks and risk indicators; and (vi) providing the MIHIC risk score andinsights to understanding behavior of risk factors for cohorts asgraphical presentations through a dash board.

DETAILED DESCRIPTION OF THE INVENTION

The MIHIC system comprises an algorithm and a method to determine a riskscore between 0 and 1 (derived by applying an algorithm to processprobabilities of risk). The MIHIC system uses self-learning modelsincluding, but not limited to, reinforcement learning to continuallyimprove the prediction of score and stratification of risk level as Low,Medium and High. MIHIC score is a quantification of identified possiblerisks to the mother, fetus and infant. The score uses a specializedalgorithm to derive the individual and overall risk as a value between 0and 1.

MIHIC system as disclosed herein determines the propensity for maternal,fetal and infant risk based on a score predicted given the medical,clinical and biological characteristics of pregnant women. Computingsuch an individual risk score for each pregnant mother for maternal,fetal and infant risk can aid the Clinicians/Care-givers in clinicaldecision making for assessment of maternal health and timelyintervention to prevent pregnancy related complications. The MIHICecosystem has various components with a suite of Artificial Intelligencealgorithms developed using software such as Python and R. FIG. 1includes the overview of the MIHIC system in various embodiments of theclaimed invention. Designing and developing such a complex computationsystem for predicting risks utilizes a software ecosystem/platform withrobust computational infrastructure encompassing components for:

-   -   Gathering data from multiple sources—Nurses, Doctors,        Clinicians, Labs, Hospitals etc.;    -   Using diverse technologies for collection, processing, storage        and distribution of data such as Smart Phones, iPads,        Desktop/Personal Computers, Stand-alone/On-Premise/Cloud Servers        etc.;    -   Organizing data in a main storage and in auxiliary storage        devices;    -   Data preprocessing analytics; Cleansing of data stored in        databases is known to improve performance of subsequent        processes (analytics and prediction) in the pipeline;    -   Dashboard for rendering insights from analytics;    -   A suite of Artificial Intelligence {AI} and Machine Learning        {ML} algorithms for learning knowledge to predict risks; and    -   Web interface that displays maternal health given the        characteristics of pregnant women using the knowledge from AI        algorithms.

Input—Data Acquisition

MIHIC system acquires data from multiple sources as the patient/pregnantwoman's characteristics are captured and stored by different entities atvarious places, multiple times. In general, pregnant women'scharacteristics are categorized under demographic, medical, clinical andgenetic. Some of the pregnant women's characteristics used in the MIHICsystem for gaining insights into maternal and infant risks are:

-   -   Demographic: Age, Parity, Race, Socioeconomic status, Home        Owner, Occupation, Lifestyle, County, Social Network;    -   Medical: Gestational Weeks BMI, Family Medical History,        Trimester, Blood Pressure (mmHg), Diabetes mellitus, Haemoglobin        (g/dL), Foetal Heartrate (bpm), Symptoms, Diagnosis;    -   Clinical: Urine Protein (mg/24 hour), Protein/Creatinine ratio,        Serum creatinine (mg/dL), Serum uric acid (mg/dL), Indirect        bilirubin (mg/dL), Lactate Dehydrogenase LDH (U/L), Platelet        count (/mm3), Fibrinogen (g/L), Glucose Plasma (mg/dL),        Prothrombin time-plasma (seconds);    -   Genetic: Biomarker VEGF (Vasclular Endothelial Growth Factor),        PIGF (Placental Growth Factor), sFit-1 (soluble fms-like        tyrosine kinase 1), Seng (Soluble endoglin).        Each of these characteristics are captured in different data        formats. Some of these characteristics are stored in a        structured format/represented tabularly. However, information        about laboratory tests and results are represented as reports        while Clinicians' notes are often textual, audio, video files.        Furthermore, all of the patients' medical scans are in image        format. Some of the clinical characteristic can be captured and        stored in a data format such as CSV/Excel/JSON/XML/PDF/TXT, but        ultrasound scans are stored in unstructured image format.        Further, summarization of textual data from laboratory reports        and clinical notes can be performed by applying pre-processing        techniques.

The MIHIC system leverages a suite of Machine/Deep Learning algorithmsfor exploration of factors associated with risk and subsequentlycomputes the scores for various types of risks. The system adopts andstacks numerous techniques for performing the tasks such aspre-processing, exploratory analysis and prediction of risk score.

Usually, the characteristics of the mother are obtained from multiplesources and as illustrated in FIG. 2 , the data formats vary betweensources depending upon how the data is stored and organized. Medicaldata is well known to be inaccurate, noisy and inconsistent due to thenature of data acquisition process and diversity of the nature of data.

Therefore, the MIHIC system runs its pre-processing algorithms that dealwith processing both structured and unstructured data. These algorithmspre-process the structured, unstructured and image data and then forwardthe cleaned data to the subsequent modules for further processing andanalysis. Some of the challenges in cleaning medical data andpre-processing approaches are:

Structured Data

-   -   Data Inaccuracy—handling the incomplete, missing values can be        done using traditional techniques such as imputation with mean,        normal values and also with model-based approaches such as        multivariate regression and k-nearest neighbor.    -   Data Noise—reducing noise by removing erroneous data and        outliers from the data by multivariate approaches using        different similarity measures such as Mahalanobis and Cook.    -   Data Inconsistency—identified when data is input from various        sources. During this time the source with the most inconsistent        data can be identified and can be addressed using correlation        analysis.

Unstructured Data

Dr. Notes/Text: For textual data the normalization can be a task foranalysis of clinical notes and patient's laboratory reports. Withnormalization, MIHIC system handles some of the challenges in textprocessing such as:

-   -   Format/Code Conversion—data from multiple sources in various        formats/codes can be collected and converted to simple format.        MIHIC system incorporates Scripts for converting files in        different formats to one standard format.    -   Eliminating Stop Words/Punctuations/Non-ASCII characters—MIHIC        system incorporates regular expression scripts to eliminate the        stop words, punctuations and non-ascii characters.    -   Identifying Stem Words—reducing each word in the text to base or        root will improve the analysis of textual data. MIHIC system        comprises modules for performing stemming on clinical and        laboratory notes.    -   Lemmatization—as used herein can refer to reducing words to base        form by considering the context along with the content is known        as lemmatization and can be useful in identifying clinical,        biological entities in notes or reports. Alternatively,        lemmatization of words helps to tag the text.

Medical Scans/Images

For processing medical images, the MIHIC system can provide modules toperform the following tasks:

-   -   Image Resize & Normalization—Images of different patients        collected from different sources usually have different        dimensions that are to be resized. According to various        embodiments the MIHIC system encompasses methods such as nearest        neighbor and neural networks to perform up-scaling and        down-scaling of images and also methods for transformation.    -   Noise Reduction—Noise in the medical images occurs due to        variation in capturing and can be undesirable for image        analysis. Therefore, the MIHIC system comprises techniques that        supports reduction of various types of noises including, but not        limited to, Pepper, Gaussian and Poisson. According to various        embodiments the MIHIC system comprises Neural networks-based        modules to suppress the noise in scanned images.    -   Blur—Along with noise the other major distorter for quality of        an image is blur and results in affecting the accuracy of the        prediction models. According to one embodiment the MIHIC system        comprises Kernel filters such as gaussian blur, deep neural        networks, to sharpen and blur the images during the training of        the prediction model. Consequently, during real time prediction        the model would have acquired resistance to blurring in the        medical images.    -   EDA—Exploratory Data Analysis: MIHIC system also considers the        synthesized results pertaining to the factors associated with        maternal, fetal and infant risks. These results can show the        incidence and prevalence of the factors for risks besides        providing deep insights into understanding the behavior of risk        factors for different cohorts. Such an exploratory analysis can        be used by Clinicians in designing the prevention and        intervention strategies. Results can be rendered by rich        graphical presentations through a dashboard that enables easy        interpretation and assessment of risk indicators. Some of the        visualizations rendered in the dashboard include, but are not        limited to:

Visualization of indicators such as maternal mortality rate, infantmortality rate, spread of maternal mortality by geographical area etc.are usually depicted using the charts speedometer, gauge meter andhorizontal bar charts.

FIG. 6 graphically illustrates maternal and infant mortality rates. Forexample, the interactive dashboard has speedometers depicting number ofmaternal deaths per 100,000 births and number of infant deaths per 1000births. FIG. 7 graphically illustrates causes of death in pregnantwomen. Exemplary causes of death depicted using a donut chart areeclampsia, high fever and hemorrhage. FIG. 8 graphically illustratestypes of delivery in pregnant women as percentage of preterm andfull-term deliveries using a pie chart.

Prevalence of certain risk factors by demographic characteristic ofpatients can be illustrated using distribution charts, boxplots, violinplots, pie and bar charts.

FIG. 9 illustrates MIHIC scores of pregnant women in 11 sub-locationswithin a demarcated geographical region. Listed are the number ofpregnant women having propensity for low, medium and high-risk levelsfor maternal and infant risk within each of the 11 geographicalsub-locations. The data supports the hypothesis that proximity toquality healthcare within a geographical region may be insufficient toresult in receipt of adequate healthcare as evidenced by the starkdifferences in the number of high risk pregnancies compared to combinednumbers of low and medium risk pregnancies in a given sub-location. Forexample, a comparison of Geolocation 8 vs. Geolocation 4 indicates that79% ( 15/19) of the pregnancies were high risk in Geolocation 8 but only50% ( 11/22) were high risk pregnancies in Geolocation 4. FIG. 10illustrates the percentage of women having risk for the listed pregnancycomplications considered for maternal and infant risks. For example, thestacked column chart depicts percentage of women having propensity to alow, medium and high levels of risks to the complications of anemia,congenital disease (CD), gestational diabetes (GDM), preeclampsia (PE),postpartum hemorrhage (PPH), prolonged labour (ProL), sepsis and pretermlabour (PTL).

Association between risk factors and clinical characteristics can beillustrated using scatter plots, correlation matrices depicting thedegree of association and their impact on the maternal and infant risks.

Comparison charts such as column, bar and line charts help inunderstanding behavior of different cohorts of interest with each otherand also within the population with respect to the factors of interest.

FIG. 11 is a graphical illustration of percentage of women at risk byRace. A stacked bar chart is utilized to depict risk levels of fourraces—Race 1, Race 2, Race 3 and Race 4. All the pregnant women fromfour races have propensity of high maternal risk; However, data analysisreveals that women belonging to Race 2 are more prone to maternal risksthan women from other races. FIG. 12 illustrates association of riskfactor “urine protein” with the different risk levels of “maternalpreeclampsia (PE)”, depicted using violin charts. It conveys that womenwith high risk for PE have Urine protein values distributed across themean with 360 mg/24 hr and have minimum and maximum values around 290mg/24 hr and 420 mg/24 hr, respectively. Urine protein values of thewomen who are at medium and low risks for PE are with mean 260 mg/24 hrand 200 mg/24 hr, respectively. FIG. 13 depicts association of Placentalgrowth factor (PIGF) with the different risk levels of maternalpreeclampsia (PE). Violin charts depicted provide an understanding forthe distribution of PIGF values across different risks levels of PE.From the chart it can be interpreted that PIGF values decline withincrease in the risk for PE. Among the women having high risk for PE,PIGF values are centered around 500 pg/ml, while mean for medium and lowrisk populations are centered around 620 pg/ml and 710 pg/ml,respectively. FIG. 14 includes the graphical illustration of associationof mother's blood pressure and foetal heart rate across mothers havingdifferent risk levels of preeclampsia depicted in a scatter plot. Theirstrength of the relationship can be assessed thus: in the women havinglow risk for PE, the relationship between the blood pressure and heartrate is linear and strongly correlated. But in case of other groups themedium and high risk relationship is not linear and has high numbers ofoutliers. FIG. 15 depicts a scatter plot representing the association ofUrine protein and protein creatinine ratio (PCR) across mothers havingdifferent risk levels of preeclampsia. The chart shows type ofcorrelation and strength of relationship for the two preeclampsia riskfactors. For example, in the women having high risk for preeclampsia therelationship between Urine protein and PCR is linear, with strongpositive correlation. There are a moderate number of outliers present inthis relationship. Furthermore, in women with medium risk forpreeclampsia, the plot shows very strong linear association with asmaller number of outliers. Relationship between the two risk factorsfor women having low risk for preeclampsia is weak, with relationshipbeing non-linear and with a high number of outliers.

FIG. 16 includes a graphical illustration showing the association of twopregnancy complication risk scores for accessing maternal risk. Forexample, the correlation plot shows how the scores of a risk can beassociated with another risk considered. Complications compared wereanemia, congenital disease (CD), gestational diabetes (GDM),preeclampsia (PE), postpartum haemorrhage (PPH), prolonged labour,sepsis and preterm labour (PTL).

Artificial Intelligence for Prediction: In various embodiments of theclaimed invention, the MIHIC system as disclosed herein strives topredict the likelihood of maternal, fetal and infant risks. Forpredicting each risk, the system employs a suite of Artificialintelligence techniques to determine a risk score with values between 0and 1. These techniques can be trained on millions of medical recordshaving medical, clinical and biological characteristics of the mothersto attain the ability to generalize maternal and infant risks.Pre-processed data can be forwarded to the AI Suite for learning how togeneralize and predict. The Suite can have a set of machine learningtechniques that learn how to preprocess the data, extract informationfrom the texts and images and subsequently assemble the knowledge toperform risk prediction.

Representation Learning: Performance of the prediction model dependsupon the quality of data pooled for training the model. Deep neuralnetwork models are trained to learn data representation for the dataconsidered as the input. To improve the performance of the predictionmodel, vector representation can be adopted to denote the content in themedical records. Furthermore, information extracted from clinical notesand ultrasound scans are also combined with the other characteristics ofthe data and are represented as vectors.

Learning & Extracting from Text Data: The AI suite of the MIHIC systemhas neural network models of type recurrent neural networks to performthe task of extracting information from the unstructured data such aslab reports, Clinician's notes etc. Model can be trained to identify theclinical concepts in text and map them to the standard clinicalapproaches. Thereby trained model enables transformation of unstructuredtext to information represented in vectors.

Learning & Extracting from Image Data: The AI suite of the MIHIC systemcomprises deep neural network models of type convolutional neuralnetworks to perform the extraction of information from different typesof scans such as ultra sound, MRI etc. Networks are trained to learnobject segmentation from the scanned images. Once trained, the model hasthe ability to detect objects from the knowledge it has gained aboutimage features. Upon extraction of the object from the scanned image,information about the properties of the object are represented invectors.

Learning to Predict the MIHIC Score

Information extracted from the above deep neural networks can then bepassed to the stacked neural networks with deep hidden layers. Theselayers have large number of nodes with non-linear activation functionsand thus have the ability to capture the non-linear association with thevarious data characteristics of the mother. Projection of mother'scharacteristics to the higher dimension will enhance the opportunity tobetter understand the association between different characteristics.Training of the model is done in the context of supervised learning.Consequently, the model's ability to identify and extract patterns fromthe mother's characteristics pertaining to a maternal risk can bereliable with statistical significance. Further, training of the entirestacked deep network can be repeated to identify optimal values ofepochs and batch size.

Techniques such as dropout and regularization can be utilized to reducethe bias in the model's prediction and increase its capability togeneralize knowledge from the various characteristics to predict a risk.Further, the model's hyper parameters such as depth of the network,dimensions, learning rate and momentum can be fine-tuned to improvetheir power of predictability of risk by leveraging the optimizationtechniques including, but not limited to, gradient descent, stochasticgradient descent and their flavors.

Each model in the MIHIC system computes a risk score for maternal, fetaland infant risks considered, to provide a naive measure thatcomprehensively summarizes the maternal health in the form of MIHICscore. The MIHIC system, in various embodiments of the claimed inventioncan, then compute the MIHIC score using statistical techniques thatderive the score from each of the above models.

Risk Stratification and Insight Delivery: To increase the viability ofscore interpretation, the prediction results can be stratified into Low,Medium and High. This is done by the MIHIC system by employing modulesof statistical techniques to perform operations such as normalization,standardization of predicted values and identification of thresholds toclassify a risk score as low, medium and high. Such a classification ofrisk score, in various embodiments of the claimed invention, can help ineasy assessment and interpretation of maternal health of a pregnantwoman by all the stake holders of the health care system.

Feed-back layer: In addition, the MIHIC system also provides itsalgorithms the self-learning capabilities to learn continuously from thedata provided. Such an ability in various embodiments of the claimedinvention can be potentially useful in identifying and designing optimalintervention/treatment strategies.

The MIHIC Algorithm

The output of the individual risk models gives the probability of aparticular risk for e.g. Miscarriage occurring in an individual patientbased on the patient's data.

To convert probability to MIHIC score, we used the following algorithm:

-   -   max=highest probability for patient in training data set    -   min=least probability for patient in training data set

MIHIC score for individual risk=(probability of risk for thepatient−min)/(max−min)

To get MIHIC risk level, we used the following algorithm:

Count number of patients with that risk in the training data set E.g. if400 patients in training data set have anemia, then 400 highest MIHICscores should be assigned high risk.

Assign equal numbers to Remaining 2 risk level: medium and low.

This gives the cut off MIHIC scores between high-medium risk andmedium-low risk which can be used to stratify new patients into the risklevels. As more data becomes available, these cut off points will changeas the algorithm learns from the training data.

The MIHIC system calculates the overall MIHIC score by:

-   -   1. Computing the average of all model probabilities per patient        in the training data set,    -   2. Determining the min, max probability and using it to get        overall MIHIC score as follows—        -   a. Overall MIHIC risk score=(average probability of all            risks for the patient−min)/(max−min)        -   b. Overall MIHIC risk level—low, medium, and high            stratification can be determined similar to the individual            MIHIC risk levels shown above. That is, highest 30% of the            overall MIHIC scores are categorized as High risk, the next            35% scores are categorized as Medium risk and the lowest            scores 35% are categorized as Low risk.

The MIHIC system takes into account each data parameter for thematernal, fetal and infant/neonatal risks to compute the overall MIHICscore. It quantifies the entirety of maternal, fetal and infant/neonatalrisks into a single value for stratification of the overall maternalrisk and allows early intervention in high risk pregnant women for allcommon maternal, fetal and infant/neonatal risks. Further, the MIHICsystem provides a specific and unique way to predict the maternal, fetaland infant/neonatal risks in pregnant mothers as well as mothers who areplanning to conceive pregnancy. It includes a gamut of 48 comprehensiverisks covering mother, fetus and infant and can be expanded further.

Mathematical Models Used in the MIHIC Algorithm

Given a dataset having characteristics of the pregnant women along withoutcomes of delivery, the MIHIC platform processes the data foraugmentation and forwards it to the AI suite consisting of variousmodels to gain knowledge by assessing the maternal, fetal and infantconditions. Consequently, the platform formulates a machine learningproblem out of the maternal health assessment that the AI Suiteprocesses for insights. FIG. 5 illustrates the MIHIC system forobtaining the MIHIC risk score.

All the medical, clinical, historical and other data captured during thepregnancy period is considered as input X to the model and outcome ofthe delivery denoted by Y is considered as output. Here the outcome canbe any of the possible maternal, fetal and infant risks. The outcome Y,has multiple real values between 0 and 1 represented as y_(i). Eachy_(i)∈Y represents propensity for a risk.

Table 1: 90 Characteristics considered for computation of MIHIC scorefor maternal health assessment.

-   -   1 Abortion 46 Maternal Mortality Rate    -   2 Age of Pregnant Women 47 Measles    -   3 Age of Last child born 48 Medical History    -   4 Anaemia 49 Maternal Death    -   5 Biomarker: VEGF 50 Mental Disorder    -   6 Biomarker: PIGF 51 Non communicable disease diagnosis    -   7 Biomarker: sEng 52 Number of Folic Acid Tablets Given    -   8 Biomarker: sFit-1 53 Number of IFA Tablets Given    -   9 Birth Defects 54 Obesity    -   10 Birth Weight 55 Obestric History    -   11 Cancer 56 Occupation    -   12 Chickenpox 57 Own's House    -   13 Complaints 58 Parity    -   14 Date of Conception 59 Past Illness of Pregnant Women    -   15 Delivery Outcomes 60 Pin code    -   16 Delivery Place 61 Platelet count (/mm3)    -   17 Diabetes Mellitus 62 Polio    -   18 Diarrhoea 63 Post Pardinal Blood Sugar    -   19 Diastolic Blood Pressure 64 Postpartum contraception method    -   20 Expected Date of Delivery 65 Postpartum Haemorrhage    -   21 Eye problem 66 Preeclampsia    -   22 Family economic status 67 Preterm Labour    -   23 Fasting Blood Sugar 68 Prolonged Labour    -   24 Fibrinogen (g/L) 69 Protein/Creatinine ratio    -   25 Fetal heart Rate 70 Proteinuria (mg/24)    -   26 Fetal Movement 71 Prothrombin time (plasma) (seconds)    -   27 Fetal Presentation 72 Race    -   28 Full-term Delivery 73 Retained Placentas    -   29 Fundal Height 74 Serum creatinine (mg/dL)    -   30 Gender of Last child born 75 Serum uric acid (mg/dL)    -   31 Gestational Age 76 Sexually Transmitted Diseases    -   32 Gestational Diabetes 77 Site of hospital    -   33 Glucose Plasma (mg/dL) 78 Skin Rashes    -   34 Haemoglobin 79 Syphilis    -   35 Health Status during Delivery 80 Systolic Blood Pressure    -   36 Height of Pregnant Women 81 Type of Delivery    -   37 High Risk Symptoms/Complaints 82 Typhoid    -   38 HIV 83 Urine Test for Albumin    -   39 HIV Status 84 Urine Test for Sugar    -   40 Hypertension 85 Uterus Size    -   41 Indirect bilirubin (mg/dL) 86 Village    -   42 Infant Mortality Rate 87 Visit Frequency    -   43 Jaundice 88 Visit Number    -   44 LDH (U/L) 89 Weight of Last child born    -   45 Leprosy 90 Weight of Pregnant Women

The input data of X, y_(i) is processed by the models for gainingknowledge about a risk. FIG. 3 illustrates computations using knowledgeabout a risk can be attained from the machine learning models using thedata that represents characteristics of all pregnant women and theirinfants.

For a considered risk k (for example, pre-eclampsia) the AI suiteemploys ‘n’ number of machine learning algorithms to learn functions ƒ₁^(k), ƒ₂ ^(k), ƒ₃ ^(k), . . . , ƒ_(n) ^(k) that can map input X tooutput y using the data points {(X₁, Ŷ₁), (X₂, Ŷ₂), . . . (X_(n),Ŷ_(n))}. Once the functions are learned by training the algorithms, theAI suite selects ƒ_(*) ^(k): X→Y that best fits the training data. Thisselection is evaluated by considering a wide range of machine learningperformance metrics. The selected model (ƒ_(*) ^(k)(X′)=y^(k)) will beleveraged for predicting the propensity score y^(k) the risk ‘k’ giventhe real data (X′₁,?) of a pregnant woman.

For understanding the working of AI suite algorithms, consider ƒ₁ ^(k)as Logistic Regression technique for modelling the machine learningproblem of predicting the risk score. The relation between in the inputX and output score y is modelled using—

y=ƒ ₁ ^(k)(X)=β₀+β₁ x ₁+β₂ x ₂+ . . . +β_(n) x _(n)

During the training, the data {(X₁, Ŷ₁), (X₂, Ŷ₂), . . . (X_(n), Ŷ_(n))}is passed to the model to learn the parameters (weights) β₀, β₁, β₂, . .. β_(n). where in β₁ can be any factor associated with x₁ whichrepresents the age, β₂ can be any factor associated with x₂ whichrepresents the economic status and similarly, β₃, β₄, . . . , β_(n)represents factors associated with various characteristics of thepregnant women in the training data.

Logistic regression uses the cost function J(θ) to estimate weights thatbest fits the training data, given by:

${J(\theta)} = {\frac{1}{2}{\sum\limits_{i = 1}^{m}\left( {{f_{1}\left( X_{i} \right)} - y_{i}} \right)^{2}}}$

Furthermore, logistic regression updates the parameters (θ) by usingθ←θ−α∇J(θ). After reducing error in prediction training will beconcluded. Advantage of logistic regression is that it not only providesweights but also their corresponding odds ratio and standard deviationerror.

AI suite algorithms can be further explained by considering ƒ_(n) ^(k)as a deep Neural Network architecture for predicting k risk (for examplepre-term labour). For each layer, z_(j) ^([i]) can be calculated usingz_(j) ^([i])=w_(j) ^([i]) ^(T) A^([i-1])+β_(j) ^([i]) where i is numberof layers for j^(th) observation, A is the input layer, w denotes theweights and β, the biases. First hidden layer A, will be a⁰=X, forsecond layer it will be output of first hidden layer and so on.Depending upon the position of each layer (hidden or output) appropriateactivation function is employed such as, sigmoid

${g({\mathcal{z}})} = \frac{1}{1 + e^{- {\mathcal{z}}}}$

or rectified linear unit ReLU g(z)=max(0, z).

For each observation, loss function L(Z,y)=−[y log(z)+(1−y)log(1−z)] iscalculated and for total m number of observations, cost function

${J\left( {w,b} \right)} = {\frac{1}{m}{\sum}_{i = 1}^{m}{L\left( {{\mathcal{z}}^{i},y^{i}} \right)}}$

is calculated. For each iteration, weights w and biases β are updatedusing the function

$\frac{\partial{L\left( {{\mathcal{z}},y} \right)}}{\partial w} = {{\frac{\partial{L\left( {{\mathcal{z}},y} \right)}}{\partial a} \times \frac{\partial a}{\partial z} \times \frac{\partial{\mathcal{z}}}{\partial w}{and}\frac{\partial{L\left( {{\mathcal{z}},y} \right)}}{\partial\beta}} = {\frac{\partial{L\left( {{\mathcal{z}},y} \right)}}{\partial a} \times \frac{\partial a}{\partial z} \times \frac{\partial z}{\partial\beta}}}$

to minimize the cost function.

In the context of medical domain, whenever there can be some uncertaintyin inferring the interactions amongst input data, leveraging deep neuralnetworks offers a good solution.

Alternatively, ƒ₁ ^(k) can be a Recurrent Neural Network (RNN) thatprocesses the textual data that captures the characteristics of thepregnant women. This textual data is transformed to informationrepresented using vectors that is compatible for processing with othercharacteristics. Modelling with RNN involves representing data insequences, the input holding textual data is forwarded to network asx=(x₁, . . . , x_(L)) of length ‘L’. The output variable y, where y=(y₁,. . . , y_(m)) is a sequence of any bounded length m. The RNN network istrained on the data of notation D={(X_((j)), y_((j)))}^(n), havingsample size as n.

At each layer, the propagation of layers in the network can berepresented using the following equation

ƒ:I _(h) ×I _(x) ×H _(T) →I _(h)

where I_(x) represents the product space of input, I_(h) product spaceof hidden states and H_(T) product space of parameters θ. To simplifyit, for any given hidden state h, input data x and estimated parametersθ.

ƒ(h,x,θ)∈I _(h)

This RNN is distinct from generic neural network in a way that at anyi^(th) layer, the inputs will be from (i−1)^(th) hidden layer. Thelayers hidden states process by h_(i)≡f(h_(i-1), x_(i), θ) prediction atthe final layer is performed using ŷ_(i)=(g∘α_(i))(h), where inα_(i)=ƒ_(i) ∘ . . . ∘ƒ₁ with each function ƒ_(i): I_(h)→I_(h)

As every layer of RNN produces an output, loss is to be computed at eachlayer. Total loss is computed at the final layer as the sum of lossesincurred at each layer as J(θ)=ΣJ(y, ŷ_(i))

Each value of the vector y, denotes a clinical entity including, but notlimited to, symptoms, medical history, age, race, test results etc. Inthis way, the unstructured information in the documents are extractedand processed along with other characteristics for prediction of risks.

Similarly, ƒ₄ ^(k) can also be any popular machine learning algorithmslike Support Vector Regression (SVR) having a generic mapping functions,y=ƒ(X)=w^(T)X+β to model the input and output data. SVR has the abilityto deal with nonlinear data and delivers a learned hyperplane from thetraining data. This hyperplane is usually more stable and does not getinfluenced by small changes in the data characteristics. Structure ofthe hyperplane is depending upon the selection of kernel, if kernel withhigh dimensions is chosen then there will be many support vectorsresulting in more time for training. During the training, the model willlearn support vectors of hyperplane by using the training data and costfunction J(w)=CΣ_(i=1) ^(N)L(y_(i), ƒ(X_(i)))+½∥w∥² which is anintensive loss function of epsilon. Optimization for getting the bestestimates of the parameters using unconstrained optimizationJ(w)=CΣ_(i=1) ^(N) (φ_(i) ⁺+φ_(i) ⁻)+½∥w∥², where φ_(i) ⁺, φ_(i) ⁻denote slack variables.

As illustrated in FIG. 4 , the MIHIC system includes in variousembodiments of the claimed invention an AI suite which executes multiplemachine learning models to find the optimum model yielding highestmetrics of evaluation. The AI suite includes, but is not limited to,models as simple as logistic regression, Support Vector Machine (SVM)regression to complex models such as neural networks including, but isnot limited to, convolutional neural network (CNN), recurrent neuralnetwork (RNN) and long short-term memory model (LSTM).

In one possible configuration of the system, all available types ofinput data (depicted in FIG. 2 ) can be used to train multiple modelsand best model will be employed for predicting multiple event outcomesrelated to maternal, fetal and infant health.

In another possible configuration of the system, multiple machinelearning model will be trained on subset of data and best ensemble ofthose models will be employed for the prediction in various embodimentsof the claimed invention. For example, CNN model will be trained usingimage scans data, RNN models will be trained using clinical notes andmedical history data and so on. Then best performing models from eachinput data types will be assessed for concordance among them and thenall those models will be ensembled or stacked together to predictingmultiple event outcomes related to maternal, fetal and infant health.

Further, the system provides a specific and unique way to predict thematernal, fetal and infant/neonatal risks in pregnant mothers as well asmothers who are planning to become pregnant. It includes a gamut of 48comprehensive risks covering mother, fetus and infant and can be expandfurther.

The Maternal risk factors include, but are not limited to, miscarriage,anemia, gestational diabetes, gestational hypertension, preeclampsia,preterm labor, preterm birth, preterm premature rupture of membranes(PPROM), placental abruption, cesarean delivery, sepsis, venousthromboembolic event, postpartum hemorrhage, postpartum depression andmultiple births.

Fetal risk factors include, but are not limited to, still birth, growthrestriction, macrosomia/excessive growth, congenital anomaly, spinabifida/anencephaly, aneuploidy, drug-induced abnormality,chorioamnionitis/intraamniotic infection and birth injury.

Infant or neonatal risk factors include, but are not limited to, lowbirth weight, excessive birth weight, neonatal anemia, neonatalhypoglycemia, intraventricular hemorrhage, respiratory distresssyndrome, bronchopulmonary dysplasia, necrotizing enterocolitis,retinopathy of prematurity/blindness, neonatal sepsis, neonataljaundice, neonatal demise, newborn encephalopathy/Hypoxic ischemicencephalopathy(HIE), neurodevelopmental delay/Cerebral Palsy(CP) andadmission to NICU (Neonatal Intensive Care Unit).

The MIHIC system goes beyond the pharmacogenomics risk factors andincludes additional dimensions including, but not limited to, patient'slife-style, demographics, drug-disease and drug-drug interactions tofurther understand a patient in complete detail and accordinglydetermine the optimal medical intervention required for each individualpatient.

Data points used by the MIHIC system include genomic data (for e.g.,genetic variants in CDKAL1 and MTNR1B genes were associated withgestational diabetes mellitus), geographical data (for e.g., Thereported Neural Tube defects prevalence ranges and medians for eachregion vary and were as follows: African (5.2-75.4; 11.7 per 10,000births), Eastern Mediterranean (2.1-124.1; 21.9 per 10,000 births),European (1.3-35.9; 9.0 per 10,000 births), Americas (3.3-27.9; 11.5 per10,000 births), South-East Asian (1.9-66.2; 15.8 per 10,000 births), andWestern Pacific (0.3-199.4; 6.9 per 10,000 births), BMI (for e.g.,obesity can cause preeclampsia, gestational diabetes mellitus,stillbirth), Blood pressure (for e.g., hypertension can causestillbirth), alcohol intake (for e.g. associated with increased risk ofspontaneous abortion), tobacco use (for e.g., tobacco users associatedwith increased risk of preterm births and stillbirths), rural residence(for e.g., rural residence associated with increased risk of infantmortality).

The MIHIC system not only uses the structured data fields like age, racebut also uses unstructured data from sources like clinical reports,social media, audio and video files of patient encounters. It uses acomprehensive list of data fields which include demographic information,clinical data (mother and fetus/infant), routine laboratory tests,investigational biomarkers, genetic testing,transcriptomic/metabolomic/proteomic biomarkers, microbiome, imagingstudies (esp. ultrasound), medications, clinical notes—by physicians,nurses, nutritional data, patient experience scores, institutionaldata—to investigate the impact of practice patterns (for ex. Examiningthe hospital protocols), physician data (for examining the impact ofindividual providers on outcome) and audio/video files ofClinician-patient Interactions.

The MIHIC system uses artificial intelligence (AI) techniques with highaccuracy of between 75-90% for different individual risk models. Themodels leverage advanced computing capabilities and are not limited to:Artificial Intelligence (including neural networks, Natural LanguageProcessing and understanding, deep learning) and traditional statisticaltechniques and can analyze structured and unstructured data setsincluding, but not limited to: bio-markers and bio-chemistry data,images, genetics, Clinician notes, audios and videos, social media data,demographic and socio-economic data.

The MIHIC system continuously receives real-time feed-back fromcaregivers and improvises the scores on a perpetual basis. It leveragescutting-edge computing capabilities of AI, Mathematics and Statistics,analyzes relevant data (example: genetics, images, Clinician's notes,audio and videos, social media, healthcare records, wearable devices,pathology etc.) and generates unparalleled insights about diseases,their evolution and the impact of interventions.

For example, severe bleeding after birth can kill a healthy woman withinhours if she is left unattended. The MIHIC system will enable theClinicians to identify the women with high risk of severe bleedingpost-delivery, based on which preventive steps can be taken, such asinjecting oxytocin immediately after childbirth to reduce the risk ofbleeding.

In another example the MIHIC system helps Clinicians recognize earlysigns of infection and enables them to prevent or eliminate—throughtimely treatment—the risks that would otherwise occur due to theprogression of such infections.

In another example the MIHIC system enables Clinicians to detect theprobability of onset of pre-eclampsia and the same can be managed beforethe onset of convulsions (eclampsia) and other resultinglife-threatening complications with a preventive treatment measure suchas administering drugs such as magnesium sulfate for pre-eclampsia, thuslowering a woman's risk.

The MIHIC system provides timely management and preventive treatmentthat can make the difference between life and death for both the motherand baby.

EXAMPLES Exemplary System of Maternal Risk

The MIHIC System receives as input in various embodiments of the claimedinvention all the demographic, clinical, social, genomic and other omicsdata about the patient. Including, but not limited to:

-   -   i. Demographic information    -   ii. Clinical data (mother and fetus/infant)    -   iii. Routine laboratory tests    -   iv. Investigational biomarkers    -   v. Genetic testing    -   vi. Transcriptomic/metabolomic/proteomic biomarkers    -   vii. Microbiome    -   viii. Imaging studies (esp. ultrasound)    -   ix. Medications    -   x. (Clinical notes—by physicians, nurses)    -   xi. (Nutritional data)    -   xii. (Patient experience scores)    -   xiii. (Institutional data—to investigate the impact of practice        patterns, e.g. Look at hospital protocols)    -   xiv. (Physician data—look at the effect of individual providers        on outcome)    -   xv. Audio/Video files of Clinician-Patient Interactions    -   xvi. Data from wearable devices

Example 1: Maternal Risk 1—Miscarriage Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having amiscarriage during the pregnancy. The MIHIC system further stratifiesthe miscarriage risk as either low, medium or high levels.

Using the MIHIC Miscarriage score and MIHIC level for miscarriage theClinicians can intervene and prevent miscarriage occurring in thehigh-risk patients by treating the preventable conditions that lead tomiscarriage including, but not limited to, uncontrolled diabetes,uterine abnormalities and avoiding invasive prenatal tests, advisingpatients against smoking of cigarettes or e-cigarettes, consumption ofalcohol and illicit drugs.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for miscarriage and not in patients at lowrisk for miscarriage.

Example 2: Maternal Risk 2—Anemia Prediction

The MIHIC System ingests the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having anemiaduring the pregnancy. The MIHIC system further stratifies the anemiarisk as either low, medium or high levels.

Using the MIHIC Anemia score and MIHIC level for anemia, Clinicians canintervene and prevent anemia occurring in the high-risk patients bytreating the preventable conditions that lead to anemia including, butnot limited to, not consuming enough iron, treat parasite infections,prevent exposure to malarial infection and advise dietary changes thatmay help prevent morning sickness and vomiting.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for anemia and not in patients at low riskfor anemia.

Example 3: Maternal Risk 3—Gestational Diabetes Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havinggestational diabetes during the pregnancy. The MIHIC system furtherstratifies the gestational diabetes risk as either low, medium or highlevels.

Using the MIHIC gestational diabetes score and MIHIC level forgestational diabetes, Clinicians can intervene and prevent gestationaldiabetes occurring in the high-risk patients by correcting thepreventable conditions that lead to gestational diabetes including, butnot limited to:

Lifestyle Interventions:

-   -   monitor weight gain in early stages of pregnancy    -   monitor blood sugar    -   quit smoking of cigarettes or e-cigarettes        increased intensity of exercise while pregnant

Dietary Interventions:

-   -   low glycemic index maternal diet    -   high fiber diet    -   consumption of probiotics    -   increased vitamin D in the maternal diet

Pharmaceutical Interventions:

-   -   administration of metformin    -   administration of insulin

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for gestational diabetes and not inpatients at low risk for gestational diabetes.

Example 4: Maternal Risk 4—Gestational Hypertension Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havinggestational hypertension during the pregnancy. The MIHIC system furtherstratifies the gestational hypertension risk as either low, medium orhigh levels.

Using the MIHIC Gestational Hypertension score and MIHIC level forgestational hypertension, Clinicians can intervene and preventgestational hypertension occurring in the high-risk patients by treatingthe gestational hypertension by taking blood pressure medication asprescribed, stay active, eat a healthy diet and avoid smoking ofcigarettes or e-cigarettes, consumption of alcohol and illicit drugs.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for gestational hypertension and not inpatients at low risk for gestational hypertension.

Example 5: Maternal Risk 5—Preeclampsia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havingpreeclampsia during the pregnancy. The MIHIC system further stratifiesthe preeclampsia risk as either low, medium or high levels.

Using the MIHIC Preeclampsia score and MIHIC level for preeclampsia,Clinicians can actively intervene and manage preeclampsia occurring inthe high-risk patients by:

Pharmaceutical Interventions:

-   -   daily dose of aspirin (85 mg) before 16 weeks of pregnancy    -   daily calcium supplement (1.5 g to 2 g)    -   administration of diuretics    -   administration of antihypertensive drugs    -   administration of oral beta blockers

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for preeclampsia and not in patients atlow risk for preeclampsia.

Example 6: Maternal Risk 6—Preterm Labor Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having pretermlabor during the pregnancy. The MIHIC system further stratifies thepreterm labor risk as either low, medium or high levels.

Using the MIHIC Preterm labor score and MIHIC level for preterm labor,Clinicians can intervene and prevent preterm labor occurring in thehigh-risk patients by treating the preventable conditions including, butnot limited to:

Pharmaceutical Interventions:

-   -   progesterone supplements for those with a history of preterm        labor        -   begin supplements at the 16th to 24th week of pregnancy        -   continue taking supplements until the 34th week of pregnancy    -   17α-hydroxyprogestrone caproate shots for those with a history        of preterm birth    -   administration of tocolytics to delay delivery        -   nifedipine recommended to block calcium channels

Surgical Interventions:

-   -   single embryo transfer for mothers undergoing IVF    -   prevent preterm delivery with cervical cerclage

Environmental Interventions:

-   -   ensure mothers avoid strenuous work    -   quit smoking of cigarettes or e-cigarettes    -   quit the use of illegal substances    -   social support and resources for pregnant victims of domestic        violence

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for preterm labor and not in patients atlow risk for preterm labor.

Example 7: Maternal Risk 7—Preterm Premature Rupture of MembranesPrediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having PretermPremature Rupture of Membranes during the pregnancy. The MIHIC systemfurther stratifies the Preterm Premature Rupture of Membranes risk aseither low, medium or high levels.

Using the MIHIC Preterm Premature Rupture of Membranes score and MIHIClevel for Preterm Premature Rupture of Membranes, Clinicians canactively intervene and prevent Preterm Premature Rupture of Membranesoccurring in the high-risk patients by treating the preventableconditions that lead to Preterm Premature Rupture of Membranesincluding, but not limited to, abnormal vaginal discharge, sexualintercourse during pregnancy, smoking of cigarettes or e-cigarettes,anemia, gestational hypertension.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for Preterm Premature Rupture of Membranesand not in patients at low risk for Preterm Premature Rupture ofMembranes.

Example 8: Maternal Risk 8—Placental Abruption Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havingplacental abruption during the pregnancy. The MIHIC system furtherstratifies the placental abruption risk as either low, medium or highlevels.

Using the MIHIC placental abruption score and MIHIC level for placentalabruption, Clinicians can actively intervene and prevent placentalabruption occurring in the high-risk patients by treating thepreventable conditions including, but not limited to:

-   -   i. Chronic high blood pressure (hypertension)    -   ii. High blood pressure during pregnancy, resulting in        preeclampsia or eclampsia    -   iii. A fall or other type of blow to the abdomen    -   iv. Smoking of cigarettes or e-cigarettes    -   v. Cocaine use during pregnancy    -   vi. Early rupture of membranes, which causes leaking amniotic        fluid before the end of pregnancy    -   vii. Infection inside of the uterus during pregnancy        (chorioamnionitis).

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for placental abruption and not inpatients at low risk for placental abruption.

Example 9: Maternal Risk 9—Caesarian Delivery Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aCaesarian delivery during the pregnancy. The MIHIC system furtherstratifies the Caesarian delivery risk as either low, medium or highlevels.

Using the MIHIC Caesarian delivery score and MIHIC level for Caesariandelivery, Clinicians can better manage Caesarian delivery occurring inthe high-risk patients.

Example 10: Maternal Risk 10—Sepsis Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having sepsisfollowing delivery. The MIHIC system further stratifies the sepsis riskas either low, medium or high levels.

Using the MIHIC Sepsis score and MIHIC level for Sepsis, Clinicians canintervene and prevent Sepsis occurring in the high-risk patients bytreating the preventable conditions including, but not limited to:

-   -   ensure hygiene of facilities    -   administration of antibiotic prophylaxis during childbirth    -   particular care during C-sections

Pharmaceutical Interventions:

-   -   administration of antibiotic prophylaxis for third and fourth        degree perineal tears    -   in the case of C-sections, administration of antibiotic        prophylaxis before the incision        -   first generation cephalosporin or penicillin is preferred    -   treatment of preterm prelabor ruptures of membranes with        antibiotics

Surgical Interventions:

-   -   in the case of C-sections, clean the vagina with povidone iodine        before the procedure

Environmental Interventions:

-   -   ensure hygiene of childbirth facilities    -   ensure that water and tools used during childbirth are properly        sanitized

Education Interventions:

-   -   train medical workers to recognize signs of maternal sepsis

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for Sepsis and not in patients at low riskfor Sepsis.

Example 11: Maternal Risk 11—Venous Thromboembolic Event Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a venousthromboembolic event during the pregnancy. The MIHIC system furtherstratifies the venous thromboembolic event risk as either low, medium orhigh levels.

The MIHIC venous thromboembolic event score and MIHIC level for venousthromboembolic event, Clinicians can intervene and prevent venousthromboembolic event occurring in the high-risk patients by treating thepreventable conditions that lead to venous thromboembolic eventincluding, but not limited to, hospitalization, infection, hyperemesis,preeclampsia, obesity, caesarean section, major postpartum bleeding, andintrauterine growth restriction or fetal death.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for venous thromboembolic event and not inpatients at low risk for venous thromboembolic event.

Example 12: Maternal Risk 12—Postpartum Hemorrhage Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having apostpartum hemorrhage during the pregnancy. The MIHIC system furtherstratifies the postpartum hemorrhage risk as either low, medium or highlevels.

Using the MIHIC postpartum hemorrhage score and MIHIC level forpostpartum hemorrhage, Clinicians can intervene and prevent postpartumhemorrhage occurring in the high-risk patients by treating thepreventable conditions including, but not limited to:

-   -   i. Placental abruption    -   ii. Overdistended uterus. This is when the uterus is larger than        normal because of too much amniotic fluid or a large baby.    -   iii. High blood pressure disorders of pregnancy    -   iv. Prolonged labor    -   v. Infection    -   vi. Obesity    -   vii. Use of forceps or vacuum-assisted delivery

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for postpartum hemorrhage and not inpatients at low risk for postpartum hemorrhage.

Example 13: Maternal Risk 13—Postpartum Depression Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havingPostpartum depression following delivery. The MIHIC system furtherstratifies the Postpartum depression risk as either low, medium or highlevels. Using the MIHIC Postpartum depression score and MIHIC level forPostpartum depression, Clinicians can actively intervene and preventPostpartum depression occurring in the high-risk patients by treatingthe preventable conditions including, but not limited to:

-   -   i. depression    -   ii. bipolar disorder    -   iii. baby has health problems or other special needs    -   iv. difficulty breast-feeding    -   v. problems in relationship with spouse or significant others    -   vi. have a weak support system    -   vii. have financial problems    -   viii. The pregnancy was unplanned or unwanted

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for Postpartum depression and not inpatients at low risk for Postpartum depression.

Example 14: Maternal Risk 14—Multiple Births Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having multiplebirths during the pregnancy. The MIHIC system further stratifies themultiple births' risk as either low, medium or high levels.

Using the MIHIC multiple births score and MIHIC level for multiplebirths, Clinicians can intervene and manage multiple births occurring inthe high-risk patients.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for multiple births and not in patients atlow risk for multiple births.

Example 15: Maternal Risk 15—Placenta Previa Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having placentaprevia during the pregnancy. The MIHIC system further stratifies theplacenta previa risk as either low, medium or high levels.

Using the MIHIC placenta previa score and MIHIC level for miscarriage,Clinicians can intervene and manage complications of placenta previaoccurring in the high-risk patients including, but not limited to,hemorrhage and shock, fetal distress from lack of oxygen.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for placenta previa and not in patients atlow risk for placenta previa.

Example 16: Maternal Risk 16—Placenta Accreta/Increta Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having placentaaccreta/increta during the pregnancy. The MIHIC system furtherstratifies the placenta accreta/increta risk as either low, medium orhigh levels.

The MIHIC placenta accreta/increta score and MIHIC level for placentaaccreta/increta, Clinicians can intervene and manage the complicationsof placenta accreta/increta occurring in the high-risk patientsincluding, but not limited to, hemorrhage, disseminated intravascularcoagulopathy, lung failure and kidney failure.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for placenta accreta/increta and not inpatients at low risk for placenta accreta/increta.

Example 17: Maternal Risk 17—Uterine Rupture Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having auterine rupture during the pregnancy. The MIHIC system furtherstratifies the risk of uterine rupture risk as either low, medium orhigh levels.

Using the MIHIC uterine rupture score and MIHIC level for uterinerupture, Clinicians can intervene and prevent uterine rupture occurringin the high-risk patients by treating the preventable conditions thatlead to uterine rupture including, but not limited to, use of oxytocin,induction of labor.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for uterine rupture and not in patients atlow risk for uterine rupture.

Example 18: Maternal Risk 18—Admission to ICU Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havingadmission to ICU during the pregnancy. The MIHIC system furtherstratifies the admission to ICU risk as either low, medium or highlevels.

Using the MIHIC admission to ICU score and MIHIC level for admission toICU, Clinicians can intervene and prevent admission to ICU occurring inthe high-risk patients by treating the preventable conditions that leadto admission to ICU including, but not limited to, no/irregularAntenatal care, hypertension, heart disease, fatty liver, gestationaldiabetes, thrombocytopenia, oligohydramnios.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for admission to ICU and not in patientsat low risk for admission to ICU.

Example 19: Maternal Risk 19—Maternal Death Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having maternaldeath during the pregnancy. The MIHIC system further stratifies thematernal death risk as either low, medium or high levels.

Using the MIHIC maternal death score and MIHIC level for maternal death,Clinicians can intervene and prevent maternal death occurring inhigh-risk patients by treating the conditions that lead to maternaldeath including, but not limited to, hypertension(eclampsia/preeclampsia), severe bleeding, and infections.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for maternal death and not in patients atlow risk for maternal death.

Exemplary System of Fetal Risk

The MIHIC System receives as input all available demographic, clinical,social, genomic and other omics data about the patient including, butnot limited to:

-   -   Demographic information    -   Clinical data (mother and fetus/infant)    -   Routine laboratory tests    -   Investigational biomarkers    -   Genetic testing    -   Transcriptomic/metabolomic/proteomic biomarkers    -   Microbiome    -   Imaging studies (esp. ultrasound)    -   Medications    -   Clinical notes—by physicians, nurses    -   Nutritional data    -   Patient experience scores    -   Institutional data—to investigate the effect of practice        patterns, e.g. Look at hospital protocols    -   Physician data—look at the effect of individual providers on        outcome    -   Audio/Video files of Clinician-Patient Interactions

Example 20: Fetal Risk 1—Shoulder Dystocia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the child having ashoulder dystocia. The MIHIC system further stratifies the shoulderdystocia risk as either low, medium or high levels.

The significance of MIHIC shoulder dystocia score and MIHIC level forshoulder dystocia, Clinicians can actively intervene and preventshoulder dystocia occurring in the high-risk patients by treating thepreventable conditions that lead to shoulder dystocia including, but notlimited to, gestational diabetes, obesity in mother, epiduralanesthesia.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for shoulder dystocia and not in patientsat low risk for shoulder dystocia.

Example 21: Fetal Risk 2—Stillbirth Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having astillbirth during the pregnancy. The MIHIC system further stratifies thestillbirth risk as either low, medium or high levels.

The significance of MIHIC stillbirth score and MIHIC level forstillbirth, Clinicians can actively intervene and prevent stillbirthoccurring in the high-risk patients by treating the preventableconditions including, but not limited to:

Maternal Characteristics:

-   -   Smoking tobacco of cigarettes or e-cigarettes or marijuana        during or just before pregnancy, or exposure to secondhand smoke        during pregnancy    -   Using illegal drugs before or during pregnancy

Maternal Medical Conditions:

-   -   Being overweight or obese    -   Diabetes before pregnancy    -   High blood pressure before pregnancy

Fetal Characteristics:

-   -   Small size in the fetus, given its age (sometimes called small        for gestational age [SGA]).

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for stillbirth and not in patients at lowrisk for stillbirth.

Example 22: Fetal Risk 3—Growth Restriction Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a growthrestriction during the pregnancy. The MIHIC system further stratifiesthe growth restriction risk as either low, medium or high levels.

The significance of MIHIC growth restriction score and MIHIC level forgrowth restriction, Clinicians can actively intervene and prevent growthrestriction occurring in the high-risk patients by treating thepreventable conditions that lead to growth restriction including, butnot limited to, maternal factors [weight (very low and also increasedbody mass index), smoking of cigarettes or e-cigarettes, use ofrecreational drugs, gestational hypertension, inherited or acquiredthrombophilia, anemia, autoimmune disorders (phospholipid syndrome,lupus erythematosus), antepartum diabetes mellitus, chronic diseases(chronic pulmonary disease, cyanotic heart disease)], fetal factors[congenital infections (Cytomegalovirus, Syphilis, Rubella, Varicella,Toxoplasmosis, Tuberculosis, HIV, Malaria)], adnexal factors [uterinemalformations, subchorionic hematoma, extensive villous infarction,marginal or velamentous cord insertion, placental mosaicism]

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for growth restriction and not in patientsat low risk for growth restriction.

Example 23: Fetal Risk 4—Macrosomia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith macrosomia during the pregnancy. The MIHIC system furtherstratifies the macrosomia risk as either low, medium or high levels.

The significance of MIHIC macrosomia score and MIHIC level formacrosomia, Clinicians can intervene and prevent macrosomia occurring inthe high-risk patients by treating the preventable conditions that leadto macrosomia including, but not limited to, maternal obesity, maternaldiabetes, excessive weight gain during pregnancy, overdue pregnancy.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for macrosomia and not in patients at lowrisk for macrosomia.

Example 24: Fetal Risk 5—Congenital Anomaly Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith congenital anomaly during the pregnancy. The MIHIC system furtherstratifies the congenital anomaly risk as either low, medium or highlevels.

The significance of MIHIC congenital anomaly score and MIHIC level forcongenital anomaly, Clinicians can intervene and prevent congenitalanomaly occurring in the high-risk patients by treating the preventableconditions that lead to congenital anomaly including, but not limitedto, maternal exposure to certain pesticides and other chemicals, as wellas certain medications, alcohol, tobacco and radiation during pregnancy,maternal infections such as zika virus, syphilis and rubella,nutritional deficiency, alcohol consumption.

Also, healthcare utilization can be driven down by driving preventivemeasures and/or interventions only in patients at high risk forcongenital anomaly and not in patients at low risk for congenitalanomaly.

Example 25: Fetal Risk 6—Neural Tube Defects Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith neural tube defects during the pregnancy. The MIHIC system furtherstratifies the neural tube defects risk as either low, medium or highlevels.

The significance of MIHIC neural tube defects score and MIHIC level forneural tube defects, Clinicians can intervene and prevent neural tubedefects occurring in the high-risk patients by treating the preventableconditions that lead to neural tube defects including, but not limitedto, inadequate intake of folate.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for neural tube defects and not inpatients at low risk for neural tube defects.

Example 26: Fetal Risk 7—Aneuploidy Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith aneuploidy. The MIHIC system further stratifies the aneuploidy riskas either low, medium or high levels.

The significance of MIHIC aneuploidy score and MIHIC level foraneuploidy, Clinicians can intervene if aneuploidy occurs in thehigh-risk patients by diagnosing aneuploidy by prenatal screening.

Also, healthcare utilization can be driven down by driving genetictesting only in patients at high risk for aneuploidy and not in patientsat low risk for aneuploidy.

Example 27: Fetal Risk 8—Drug-Induced Abnormality Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith drug-induced abnormality during the pregnancy. The MIHIC systemfurther stratifies the drug-induced abnormality risk as either low,medium or high levels.

The significance of MIHIC drug-induced abnormality score and MIHIC levelfor drug-induced abnormality, Clinicians can intervene and preventdrug-induced abnormality occurring in the high-risk patients by avoidingconditions that lead to drug-induced abnormality including, but notlimited to, avoiding administration of teratogenic drugs.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for drug-induced abnormality and not inpatients at low risk for drug-induced abnormality.

Example 28: Fetal Risk 9—Chorioamnionitis/Intraamniotic InfectionPrediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother havingchorioamnionitis/intraamniotic infection during the pregnancy. The MIHICsystem further stratifies the chorioamnionitis/intraamniotic infectionrisk as either low, medium or high levels.

The significance of MIHIC chorioamnionitis/intraamniotic infection scoreand MIHIC level for chorioamnionitis/intraamniotic infection, Clinicianscan intervene and prevent chorioamnionitis/intraamniotic infectionoccurring in the high-risk patients by treating the preventableconditions that lead to chorioamnionitis/intraamniotic infectionincluding, but not limited to, longer duration of membrane rupture,prolonged labor, internal monitoring of labor, multiple vaginal exams,meconium-stained amniotic fluid, smoking of cigarettes or e-cigarettes,alcohol or drug abuse, immune-compromised states.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for chorioamnionitis/intraamnioticinfection and not in patients at low risk forchorioamnionitis/intraamniotic infection.

Example 29: Fetal Risk 10—Birth Injury Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having a fetuswith birth injury during the pregnancy. The MIHIC system furtherstratifies the birth injury risk as either low, medium or high levels.

The significance of MIHIC birth injury score and MIHIC level for birthinjury, Clinicians can intervene and prevent birth injury occurring inthe high-risk patients by treating the preventable conditions that leadto birth injury including, but not limited to, macrosomia, prolongedlabor, cephalo-pelvic disproportion.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for birth injury and not in patients atlow risk for birth injury.

Exemplary System of Infant Risk

The MIHIC System receives as input all available demographic, clinical,social, genomic and other omics data about the patient including, butnot limited to:

-   -   i. Demographic information    -   ii. Clinical data (mother and fetus/infant)    -   iii. Routine laboratory tests    -   iv. Investigational biomarkers    -   v. Genetic testing    -   vi. Transcriptomic/metabolomic/proteomic biomarkers    -   vii. Microbiome    -   viii. Imaging studies (esp. ultrasound)    -   ix. Medications    -   x. (Clinical notes—by physicians, nurses)    -   xi. (Nutritional data)    -   xii. (Patient experience scores)    -   xiii. (Institutional data—to investigate the effect of practice        patterns, e.g. Look at hospital protocols)    -   xiv. (Physician data—look at the effect of individual providers        on outcome)    -   xv. Audio/Video files of Clinicians-Patient Interactions

Example 30: Infant Risk 1—Preterm Birth Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the infant with pretermbirth. The MIHIC system further stratifies the preterm birth risk aseither low, medium or high levels.

The significance of MIHIC Preterm birth score and MIHIC level forpreterm birth, Clinicians can intervene and manage preterm birthoccurring in the high-risk patients by treating the preventableconditions in the mother that lead to preterm birth including, but notlimited to:

-   -   i. Being overweight or underweight    -   ii. Smoking of cigarettes or e-cigarettes or illicit drug use    -   iii. Problems with the uterus or cervix    -   iv. Uterine or kidney infection    -   v. High blood pressure    -   vi. Having a lot of stress

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for preterm birth and not in patients atlow risk for preterm birth.

Example 31: Infant Risk 2—Low Birth Weight Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with low birth weight. The MIHIC system further stratifies thelow birth weight risk as either low, medium or high levels.

The significance of MIHIC low birth weight score and MIHIC level for lowbirth weight, Clinicians can intervene and prevent low birth weightoccurring in the high-risk patients by treating the preventableconditions that lead to low birth weight including, but not limited to,poor maternal nutrition, poor antenatal care, maternal smoking ofcigarettes or e-cigarettes, and exposure to known toxic heavy metalssuch as, lead, mercury, arsenic, cadmium and selenium.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for low birth weight and not in patientsat low risk for low birth weight.

Example 32: Infant Risk 3—Excessive Birth Weight Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with excessive birth weight during the pregnancy. The MIHICsystem further stratifies the excessive birth weight risk as either low,medium or high levels.

The significance of MIHIC excessive birth weight score and MIHIC levelfor excessive birth weight, Clinicians can intervene and preventexcessive birth weight occurring in the high-risk patients by treatingthe preventable conditions that lead to excessive birth weightincluding, but not limited to, maternal diabetes, maternal obesity,excessive weight gain during the pregnancy, overdue pregnancy.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for excessive birth weight and not inpatients at low risk for excessive birth weight.

Example 33: Infant Risk 4—Anemia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with anemia during the pregnancy. The MIHIC system furtherstratifies the anemia risk as either low, medium or high levels.

The significance of MIHIC infant anemia score and MIHIC level for infantanemia, Clinicians can intervene and prevent anemia occurring in infantsin high-risk patients by treating the preventable conditions that leadto anemia in infants including, but not limited to, blood loss, maternaldiet low in iron, prenatal vitamins, and iron supplements.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for anemia in infants and not in patientsat low risk for anemia.

Example 34: Infant Risk 5—Hypoglycemia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with hypoglycemia during the pregnancy. The MIHIC system furtherstratifies the infant hypoglycemia risk as either low, medium or highlevels.

The significance of MIHIC infant hypoglycemia score and MIHIC level forinfant hypoglycemia, Clinicians can intervene and prevent hypoglycemiaoccurring in infants in high-risk patients by treating the preventableconditions that lead to hypoglycemia including, but not limited to,prematurity, small for gestational age, maternal diabetes, perinatalasphyxia, deficient glycogen stores, delayed feeding, hyperinsulinemia.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for infant hypoglycemia and not inpatients at low risk for infant hypoglycemia.

Example 35: Infant Risk 6—Intraventricular Hemorrhage Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with intraventricular hemorrhage. The MIHIC system furtherstratifies the intraventricular hemorrhage risk as either low, medium orhigh levels.

The significance of MIHIC intraventricular hemorrhage score and MIHIClevel for intraventricular hemorrhage, Clinicians can intervene andprevent intraventricular hemorrhage occurring in the high-risk patientsby treating the preventable conditions that lead to intraventricularhemorrhage including, but not limited to, low birth weight andgestational age, maternal smoking of cigarettes or e-cigarettes,premature rupture of membranes, intrauterine infections, prolongedlabor, postnatal resuscitation and intubation, transferal from one unitto another, early onset of sepsis, development of respiratory distresssyndrome or pneumothorax, recurrent endotracheal suctioning, metabolicacidosis and rapid bicarbonate infusion, and high-frequency ventilation.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for infants with intraventricularhemorrhage and not in patients at low risk for intraventricularhemorrhage.

Example 36: Infant Risk 7—Respiratory Distress Syndrome Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with respiratory distress syndrome during the pregnancy. TheMIHIC system further stratifies the respiratory distress syndrome riskas either low, medium or high levels.

The significance of MIHIC respiratory distress syndrome score and MIHIClevel for respiratory distress syndrome, Clinicians can intervene andprevent respiratory distress syndrome occurring in the high-riskpatients by treating the preventable conditions that lead to respiratorydistress syndrome including, but not limited to, prematurity, maternaldiabetes, cesarean delivery, asphyxia.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for respiratory distress syndrome and notin patients at low risk for respiratory distress syndrome.

Example 37: Infant Risk 8—Bronchopulmonary Dysplasia Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with bronchopulmonary dysplasia during the pregnancy. The MIHICsystem further stratifies the bronchopulmonary dysplasia risk as eitherlow, medium or high levels.

The significance of MIHIC bronchopulmonary dysplasia score and MIHIClevel for bronchopulmonary dysplasia, Clinicians can intervene andprevent bronchopulmonary dysplasia occurring in the high-risk patientsby treating the preventable conditions that lead to bronchopulmonarydysplasia including, but not limited to, prematurity, prolongedmechanical ventilation, administration of high concentration of oxygen,maternal smoking of cigarettes or e-cigarettes or use of illicit drugs,maternal malnutrition, and infections in the mother during thepregnancy, patent ductus arteriosus, intra-uterine growth retardation.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for bronchopulmonary dysplasia and not inpatients at low risk for bronchopulmonary dysplasia.

Example 38: Infant Risk 9—Necrotizing Enterocolitis Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with necrotizing enterocolitis during the pregnancy. The MIHICsystem further stratifies the necrotizing enterocolitis risk as eitherlow, medium or high levels.

The significance of MIHIC necrotizing enterocolitis score and MIHIClevel for necrotizing enterocolitis, Clinicians can intervene andprevent necrotizing enterocolitis occurring in the high-risk patients bytreating the preventable conditions that lead to necrotizingenterocolitis including, but not limited to, intrauterine growthretardation, polycythemia, hypoglycemia, sepsis, exchange transfusions,umbilical lines, gestational diabetes, and being born to a mother withchorioamnionitis

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for necrotizing enterocolitis and not inpatients at low risk for necrotizing enterocolitis.

Example 39: Infant Risk 10—Retinopathy of Prematurity Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with retinopathy of prematurity during the pregnancy. The MIHICsystem further stratifies the retinopathy of prematurity risk as eitherlow, medium or high levels.

The significance of MIHIC retinopathy of prematurity score and MIHIClevel for retinopathy of prematurity, Clinicians can intervene andprevent retinopathy of prematurity occurring in the high-risk patientsby treating the preventable conditions that lead to retinopathy ofprematurity including, but not limited to, early gestational age, lowbirth weight, lower Apgar score and prolonged oxygen therapy.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for retinopathy of prematurity and not inpatients at low risk for retinopathy of prematurity.

Example 40: Infant Risk 11—Sepsis Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with sepsis during the pregnancy. The MIHIC system furtherstratifies the sepsis risk as either low, medium or high levels.

The significance of MIHIC sepsis score in infants and MIHIC level forsepsis in infants, Clinicians can intervene and prevent sepsis occurringin the high-risk patients by treating the preventable conditions thatcan lead to sepsis including, but not limited to:

-   -   i. Maternal GBS colonization (particularly in the setting of        inadequate prophylactic treatment),    -   ii. Premature rupture of membranes (PROM),    -   iii. Preterm rupture of membranes,    -   iv. Prolonged rupture of membranes,    -   v. Premature birth,    -   vi. Maternal urinary tract infection (UTI),    -   vii. Chorioamnionitis,    -   viii. Maternal fever greater than 38° C. (100.4° F.).

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for sepsis and not in patients at low riskfor sepsis.

Example 41: Infant Risk 12—Jaundice Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with jaundice during the pregnancy. The MIHIC system furtherstratifies the jaundice risk as either low, medium or high levels.

The significance of MIHIC jaundice in infants score and MIHIC level forjaundice, Clinicians can intervene and prevent jaundice occurring in thehigh-risk patients by treating the preventable conditions that can leadto jaundice including, but not limited to:

-   -   i. Preterm babies.    -   ii. Newborns with feeding difficulties/poor feeding.    -   iii. Mother with diabetes.    -   iv. Newborns with bruising/cephalohematoma.    -   v. Congenital infection.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for jaundice and not in patients at lowrisk for jaundice.

Example 42: Infant Risk 13—Demise Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant demise. The MIHIC system further stratifies the demise risk aseither low, medium or high levels.

The significance of MIHIC demise score in infants and MIHIC level fordemise, Clinicians can intervene and prevent demise occurring in thehigh-risk patients by treating the preventable conditions that lead todemise including, but not limited to, sepsis, birth asphyxia,respiratory distress syndrome, congenital anomalies.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for demise and not in patients at low riskfor demise.

Example 43: Infant Risk 14—Newborn Encephalopathy Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with newborn encephalopathy. The MIHIC system further stratifiesthe newborn encephalopathy risk as either low, medium or high levels.

The significance of MIHIC newborn encephalopathy score and MIHIC levelfor newborn encephalopathy, Clinicians can intervene and prevent newbornencephalopathy occurring in the high-risk patients by treating thepreventable conditions that lead to newborn encephalopathy including,but not limited to, maternal pyrexia, a persistent occipitoposteriorposition.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for newborn encephalopathy and not inpatients at low risk for newborn encephalopathy.

Example 44: Infant Risk 15—Neurodevelopmental Delay Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with neurodevelopmental delay during the pregnancy. The MIHICsystem further stratifies the neurodevelopmental delay risk as eitherlow, medium or high levels.

The significance of MIHIC neurodevelopmental delay score and MIHIC levelfor neurodevelopmental delay, Clinicians can intervene and preventneurodevelopmental delay occurring in the high-risk patients by treatingthe preventable conditions that lead to neurodevelopmental delayincluding, but not limited to, prematurity, infections duringpregnancy/childbirth.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for neurodevelopmental delay and not inpatients at low risk for neurodevelopmental delay.

Example 45: Infant Risk 16—Admission to NICU Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with admission to NICU during the pregnancy. The MIHIC systemfurther stratifies the admission to NICU risk as either low, medium orhigh levels.

The significance of MIHIC admission to NICU score and MIHIC level foradmission to NICU, Clinicians can intervene and prevent admission toNICU occurring in the high-risk patients by treating the preventableconditions that lead to admission to NICU including, but not limited to,operative method of birth, elective delivery before 39 weeks eithervaginally or by cesarean section, maternal diabetes and hypertension.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for admission to NICU and not in patientsat low risk for admission to NICU.

Example 46: Infant Risk 17—Length of NICU Stay Prediction

The MIHIC System consumes the input data and utilizes advanced machinelearning and statistical techniques to output a MIHIC score (a valuebetween 0 and 1) which represents the risk of the mother having aninfant with longer length of NICU stay. The MIHIC system furtherstratifies the longer length of NICU stay risk as either low, medium orhigh levels.

The significance of MIHIC length of NICU stay score and MIHIC level forlength of NICU stay, Clinicians can intervene and prevent longer lengthof NICU stay occurring in the high-risk patients by treating thepreventable conditions that lead to longer length of NICU stayincluding, but not limited to, respiratory distress syndrome,bronchopulmonary dysplasia.

Also, healthcare utilization can be driven down by driving interventionsonly in patients at high risk for longer length of NICU stay and not inpatients at low risk for longer length of NICU stay.

What is claimed is:
 1. A system comprising: a processor executing amachine learning model; and a database comprising a plurality of patientrecord data; and wherein the system is operable to: acquire, by theprocessor, the plurality of patient record data from the database,wherein the patient record data comprises a text data and an image data;identify, by the processor, a data format of the patient record data;segregate, the patient record data into a structured data and anunstructured data; pre-process, by the processor, the structured dataand the unstructured data; generate, by the processor, the machinelearning model; train, by the processor, the machine learning model withthe patient record data; receive, by the machine learning model, a newpatient record data associated with a patient, wherein the new patientrecord data comprises a first clinical data comprising patient datacomprising a patient blood pressure, a second clinical data comprisingfetal data comprising a fetal heart rate, wherein the patient is amaternal woman; analyze, by the machine learning model, the new patientrecord data to discover a pattern in the patient record data using thedatabase; predict, by the machine learning model and based on thepattern, a maternal risk score for each health risk factor of a firstplurality of health risk factors associated with the patient, a fetalrisk score for each health risk factor of a second plurality of healthrisk factors associated with a fetus of the patient, wherein thematernal risk scores each represent a probability of a maternity-relatedhealthcare event of the patient and the fetal risk scores each representa probability of a fetus-related healthcare event of the fetus of thepatient; and calculate, an overall risk score from the maternal riskscore of each of the first plurality of health risk factors and thefetal risk score of each of the second plurality of health risk factorsusing a statistical technique, wherein the overall risk score is amaternal and infant health insights and cognitive intelligence (MIHIC)score, and wherein MIHIC score represents a quantification of risk forpregnancy outcome.
 2. The system of claim 1, wherein the patient recorddata further comprises a demographic data, a medical data, social data,genomic data, omics data, and a genetic data.
 3. The system of claim 1,wherein the new patient record data further comprises a patientself-generated data, wherein the patient self-generated data comprisessocial media data, lifestyle data, and data from wearable devices. 4.The system of claim 1, wherein the machine learning model furtherenables exploration and correlation of the patient data and the fetaldata associated with the first plurality of health risk factors and thesecond plurality of health risk factors.
 5. The system of claim 1,wherein the system is operable to generate the overall risk score forcaesarian delivery.
 6. The system of claim 1, wherein the system isoperable to generate the overall risk score for postpartum depression.7. The system of claim 1, wherein the system is operable to stratify theplurality of patient record data into a plurality of cohorts based on arisk level and further classify the new patient record data into acohort from the plurality of cohorts based on the overall risk score;wherein the risk level is determined based on a grouping formed fromcategorizing values of the overall risk score; and wherein the pluralityof cohorts further enable studies for understanding behavior of eachhealth risk factor of the first plurality of health risk factorsassociated with the patient and each health risk factor of the secondplurality of health risk factors associated with the fetus of thepatient and various characteristics in the patient record data belongingin each of the plurality of cohorts.
 8. A method comprising: receiving,by a processor, a patient record data associated with a first patient,wherein the patient record data comprises a text data and an image data;identifying, by the processor, a data format of the patient record data;segregating the patient record data into a structured data and anunstructured data; pre-processing, by the processor, the structured dataand the unstructured data to clean data; generating a machine learningmodel, wherein the machine learning model is further trained with thepatient record data; training, by the processor, the machine learningmodel with the patient record data; receiving a new patient record dataassociated with a patient; wherein the new patient record data comprisesa first clinical data comprising patient data comprising a patient bloodpressure, a second clinical data comprising fetal data comprising afetal heart rate, wherein the patient is a maternal woman; analyzing, bythe machine learning model, the new patient record data to discover apattern in the new patient record data using a database; predicting, bythe machine learning model and based on the pattern, a maternal riskscore for each health risk factor of a first plurality of health riskfactors associated with the patient, and a fetal risk score for eachhealth risk factor of a second plurality of health risk factorsassociated with a fetus of the patient, wherein the maternal risk scoreseach represent a probability of a maternity-related healthcare event ofthe patient and the fetal risk scores each represent a probability of afetus-related healthcare event of the fetus of the patient; calculating,by the machine learning model, an overall risk score from the maternalrisk score of each of the first plurality of health risk factors and thefetal risk score of each of the second plurality of health risk factorsusing a statistical technique; and wherein the overall risk score is amaternal and infant health insights and cognitive intelligence (MIHIC)score, and wherein MIHIC score represents a quantification of risk forpregnancy outcome.
 9. The method of claim 8, wherein the patient recorddata further comprises a demographic data, a medical data, a socialdata, a genomic data, an omics data, and a genetic data.
 10. The methodof claim 8, wherein the new patient record data further comprises apatient self-generated data, wherein the patient self-generated datacomprises social media data, lifestyle data, and data from wearabledevices.
 11. The method of claim 8, wherein the machine learning modelfurther enables exploration and correlation of the patient data and thefetal data associated with the first plurality of health risk factorsand the second plurality of health risk factors.
 12. The method of claim8, wherein the method is operable to generate the overall risk score forcaesarian delivery.
 13. The method of claim 8, wherein the method isoperable to generate the overall risk score for postpartum depression.14. A system comprising: a processor that executes computer-executablecomponents stored in a computer-readable memory, the computer-executablecomponents comprising: an input module operable to receive a patientrecord data associated with a patient, wherein the patient record datacomprises a first clinical data comprising patient data comprising apatient blood pressure, a second clinical data comprising fetal datacomprising a fetal heart rate, wherein the patient is a maternal woman;analyze, by a first model, the patient record data, wherein the firstmodel comprises a machine learning model; predict, by the machinelearning model, a maternal risk score for each health risk factor of afirst plurality of health risk factors associated with the patient, afetal risk score for each health risk factor of a second plurality ofhealth risk factors associated with a fetus of the patient, wherein thematernal risk scores each represent a probability of a maternity-relatedhealthcare event of the patient and the fetal risk scores each representa probability of a fetus-related healthcare event of the fetus of thepatient; and compute, an overall risk score using a second model, fromthe maternal risk score of each of the first plurality of health riskfactors and the fetal risk score of each of the second plurality ofhealth risk factors, wherein the second model comprises a statisticaltechnique, wherein the overall risk score is a maternal and infanthealth insights and cognitive intelligence (MIHIC) score, and whereinMIHIC score represents a quantification of risk for pregnancy outcome.15. The system of claim 14, wherein the machine learning model comprisesa relationship derived between inputs in the patient record data and thefirst plurality of health risk factors associated with the patient, andthe second plurality of health risk factors associated with the fetus ofthe patient.
 16. The system of claim 14, wherein the machine learningmodel is trained using plurality of patient record data, wherein each ofthe plurality of patient record data comprises a demographic data, aclinical data, a medical data, a social data, a genomic data, an omicsdata, and a genetic data.
 17. The system of claim 15, wherein themachine learning model is a self-learning model comprising a feed-backlayer that enables the machine learning model to learn from the patientrecord data.
 18. The system of claim 15, wherein the machine learningmodel is further operable for: receiving a feed-back relating to anobserved healthcare event of one or more of the patient and the fetus ofthe patient; update the machine learning model with the feed-back; andupdate a database with the patient record data.
 19. The system of claim18, wherein the machine learning model is further operable to learn fromthe feed-back and continually improve a prediction of the maternal riskscore of each of the first plurality of health risk factors, the fetalrisk score of each of the second plurality of health risk factors, andthe overall risk score.
 20. The system of claim 14, wherein the systemis operable to generate the overall risk score for caesarian delivery.